DocumentCode
724290
Title
A study on autonomous learning mechanism of Cognitive robot
Author
Shi Tao ; Ren Hongge ; Yin Rui ; Xiang Yingfan
Author_Institution
Coll. of Electr. Eng., Hebei United Univ., Tangshan, China
fYear
2015
fDate
23-25 May 2015
Firstpage
3243
Lastpage
3247
Abstract
Aiming at the movement balance control problem of the robot, this paper presents a sensorimotor system Cognitive model based on operant conditioning principle, and researches the working cooperation among its interior nerve organs, so that a sensorimotor system is established. The Cognitive model can realize the sensorimotor mapping from states to actions by supervised learning, and carry out the probabilistic choice based on operant conditioning principle to actions using the action forecast evaluation results, thereby, the robot obtains the self-learning ability like human or animal through interacting, studying and training with the unknown environment, and realized the movement balance control to the robot. Consequently, the paper makes some simulation experiments on the robot, and the results indicate that this model has the better Cognitive characters and make the robot master the movement balance control skill through autonomic learning.
Keywords
cognitive systems; learning (artificial intelligence); mobile robots; motion control; probability; action forecast evaluation; autonomic learning; autonomous learning mechanism; cognitive characters; cognitive robot model; interior nerve organs; movement balance control problem; operant conditioning principle; probabilistic choice; self-learning ability; sensorimotor mapping system; supervised learning; Animals; Basal ganglia; Biological system modeling; Brain modeling; Mobile robots; Robot sensing systems; Cognitive Model; Movement Balance Control; Operant Conditioning; Robot; Sensorimotor System;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location
Qingdao
Print_ISBN
978-1-4799-7016-2
Type
conf
DOI
10.1109/CCDC.2015.7162479
Filename
7162479
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